In the field of face recognition, many people wear eyeglasses and especially deep-frame eyeglasses, which leads to high similarity of face images with deep-frame eyeglasses and the inability of accurate face recognition during face recognition. At present, the method adopted by the industry is to remove eyeglass regions in the face images and then recognize the face images after removing the eyeglass regions. However, the key of this method is how to accurately determine the eyeglass regions in the face images.
Due to the influences of diversity of eyeglass shapes, image quality and other factors, there are many difficulties in eyeglass detection. For example, in the early detection of the eyeglasses, image processing and template matching methods are mainly used; a lower frame of the eyeglasses and a nose bridge of the eyeglasses are detected according to the discontinuous change of pixel gray values; and then, the eyeglasses are detected by the edge information of a region between two eyes. In the later detection of the eyeglasses, a three-dimensional Hough transformation method is mainly used to detect the eyeglasses. However, due to the influence of different light rays, images obtained by image processing and Hough method after imaging are excessively dependent on image edges. Thus, there is noise, and noise interference leads to the failure to obtain feature points or accurate feature points. Therefore, the detection accuracy is relatively low.